
import argparse
from collections import OrderedDict
from pathlib import Path

import torch

from transformers import (
    VisualBertConfig,
    VisualBertForMultipleChoice,
    VisualBertForPreTraining,
    VisualBertForQuestionAnswering,
    VisualBertForVisualReasoning,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowercase    :   Optional[int]						=			logging.get_logger(__name__)

lowercase    :   int						=			[
    ('bert.bert', 'visual_bert'),
    ('bert.cls', 'cls'),
    ('bert.classifier', 'cls'),
    ('token_type_embeddings_visual', 'visual_token_type_embeddings'),
    ('position_embeddings_visual', 'visual_position_embeddings'),
    ('projection', 'visual_projection'),
]

lowercase    :   Any						=			[
    'nlvr2_coco_pre_trained.th',
    'nlvr2_fine_tuned.th',
    'nlvr2_pre_trained.th',
    'vcr_coco_pre_train.th',
    'vcr_fine_tune.th',
    'vcr_pre_train.th',
    'vqa_coco_pre_trained.th',
    'vqa_fine_tuned.th',
    'vqa_pre_trained.th',
]




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Any)   ->       Union[str, Any]:






							'''simple docstring'''





							__UpperCamelCase      :  int       =   torch.load(_lowerCamelCase		,    map_location="cpu")
							return sd




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Union[str, Any]		,    _lowerCamelCase					:     Dict		,    _lowerCamelCase					:     str=rename_keys_prefix)   ->       Dict:






							'''simple docstring'''





							__UpperCamelCase      :  str       =   OrderedDict()
							__UpperCamelCase      :  int       =   torch.arange(config.max_position_embeddings).expand((1, -1))
							# detector_d = OrderedDict()
							for key in d:
														if "detector" in key:
																					# detector_d[key.replace('detector.','')] = d[key]
																					continue
														__UpperCamelCase      :  Optional[int]       =   key
														for name_pair in rename_keys_prefix:
																					__UpperCamelCase      :  str       =   new_key.replace(name_pair[0]		,    name_pair[1])
														__UpperCamelCase      :  Tuple       =   d[key]
														if key == "bert.cls.predictions.decoder.weight":
																					# Old bert code didn't have `decoder.bias`, but was added separately
																					__UpperCamelCase      :  str       =   new_d["cls.predictions.bias"]
							return new_d




@torch.no_grad()
def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     str		,    _lowerCamelCase					:     Optional[Any])   ->       Dict:






							'''simple docstring'''





							assert (
							    checkpoint_path.split("/")[-1] in ACCEPTABLE_CHECKPOINTS
							), F'The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.'

							# Get Config
							if "pre" in checkpoint_path:
														__UpperCamelCase      :  Optional[Any]       =   "pretraining"
														if "vcr" in checkpoint_path:
																					__UpperCamelCase      :  int       =   {"visual_embedding_dim": 512}
														elif "vqa_advanced" in checkpoint_path:
																					__UpperCamelCase      :  Optional[int]       =   {"visual_embedding_dim": 2_048}
														elif "vqa" in checkpoint_path:
																					__UpperCamelCase      :  List[Any]       =   {"visual_embedding_dim": 2_048}
														elif "nlvr" in checkpoint_path:
																					__UpperCamelCase      :  Optional[int]       =   {"visual_embedding_dim": 1_024}
														else:
																					raise NotImplementedError(F'No implementation found for `{checkpoint_path}`.')
							else:
														if "vcr" in checkpoint_path:
																					__UpperCamelCase      :  Union[str, Any]       =   {"visual_embedding_dim": 512}
																					__UpperCamelCase      :  str       =   "multichoice"
														elif "vqa_advanced" in checkpoint_path:
																					__UpperCamelCase      :  Dict       =   {"visual_embedding_dim": 2_048}
																					__UpperCamelCase      :  Optional[Any]       =   "vqa_advanced"
														elif "vqa" in checkpoint_path:
																					__UpperCamelCase      :  List[Any]       =   {"visual_embedding_dim": 2_048, "num_labels": 3_129}
																					__UpperCamelCase      :  str       =   "vqa"
														elif "nlvr" in checkpoint_path:
																					__UpperCamelCase      :  Dict       =   {
																					    "visual_embedding_dim": 1_024,
																					    "num_labels": 2,
																					}
																					__UpperCamelCase      :  Dict       =   "nlvr"

							__UpperCamelCase      :  Optional[int]       =   VisualBertConfig(**_lowerCamelCase)

							# Load State Dict
							__UpperCamelCase      :  List[Any]       =   load_state_dict(_lowerCamelCase)

							__UpperCamelCase      :  str       =   get_new_dict(_lowerCamelCase		,    _lowerCamelCase)

							if model_type == "pretraining":
														__UpperCamelCase      :  Any       =   VisualBertForPreTraining(_lowerCamelCase)
							elif model_type == "vqa":
														__UpperCamelCase      :  List[str]       =   VisualBertForQuestionAnswering(_lowerCamelCase)
							elif model_type == "nlvr":
														__UpperCamelCase      :  Optional[Any]       =   VisualBertForVisualReasoning(_lowerCamelCase)
							elif model_type == "multichoice":
														__UpperCamelCase      :  Optional[int]       =   VisualBertForMultipleChoice(_lowerCamelCase)

							model.load_state_dict(_lowerCamelCase)
							# Save Checkpoints
							Path(_lowerCamelCase).mkdir(exist_ok=_lowerCamelCase)
							model.save_pretrained(_lowerCamelCase)


if __name__ == "__main__":
		lowercase    :   Optional[int]						=			argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('orig_checkpoint_path', type=str, help='A path to .th on local filesystem.')
		parser.add_argument('pytorch_dump_folder_path', type=str, help='Path to the output PyTorch model.')
		lowercase    :   int						=			parser.parse_args()
		convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)

# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


lowercase    :   List[Any]						=			'Run commands across TPU VMs for initial setup before running `accelerate launch`.'




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Union[str, Any]=None)   ->       List[str]:






							'''simple docstring'''





							if subparsers is not None:
														__UpperCamelCase      :  int       =   subparsers.add_parser("tpu-config"		,    description=_description)
							else:
														__UpperCamelCase      :  Dict       =   argparse.ArgumentParser("Accelerate tpu-config command"		,    description=_description)
							# Core arguments
							__UpperCamelCase      :  Optional[Any]       =   parser.add_argument_group(
							    "Config Arguments"		,    "Arguments that can be configured through `accelerate config`.")
							config_args.add_argument(
							    "--config_file"		,    type=_lowerCamelCase		,    default=_lowerCamelCase		,    help="Path to the config file to use for accelerate."		,    )
							config_args.add_argument(
							    "--tpu_name"		,    default=_lowerCamelCase		,    help="The name of the TPU to use. If not specified, will use the TPU specified in the config file."		,    )
							config_args.add_argument(
							    "--tpu_zone"		,    default=_lowerCamelCase		,    help="The zone of the TPU to use. If not specified, will use the zone specified in the config file."		,    )
							__UpperCamelCase      :  Tuple       =   parser.add_argument_group("TPU Arguments"		,    "Arguments for options ran inside the TPU.")
							pod_args.add_argument(
							    "--use_alpha"		,    action="store_true"		,    help="Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`."		,    )
							pod_args.add_argument(
							    "--command_file"		,    default=_lowerCamelCase		,    help="The path to the file containing the commands to run on the pod on startup."		,    )
							pod_args.add_argument(
							    "--command"		,    action="append"		,    nargs="+"		,    help="A command to run on the pod. Can be passed multiple times."		,    )
							pod_args.add_argument(
							    "--install_accelerate"		,    action="store_true"		,    help="Whether to install accelerate on the pod. Defaults to False."		,    )
							pod_args.add_argument(
							    "--accelerate_version"		,    default="latest"		,    help="The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify 'dev' to install from GitHub."		,    )
							pod_args.add_argument(
							    "--debug"		,    action="store_true"		,    help="If set, will print the command that would be run instead of running it.")

							if subparsers is not None:
														parser.set_defaults(func=_lowerCamelCase)
							return parser




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     List[Any])   ->       Dict:






							'''simple docstring'''





							__UpperCamelCase      :  List[str]       =   None

							# Get the default from the config file if it exists.
							if args.config_file is not None or os.path.isfile(_lowerCamelCase):
														__UpperCamelCase      :  Dict       =   load_config_from_file(args.config_file)
														if not args.command_file and defaults.command_file is not None and not args.command:
																					__UpperCamelCase      :  List[Any]       =   defaults.command_file
														if not args.command and defaults.commands is not None:
																					__UpperCamelCase      :  int       =   defaults.commands
														if not args.tpu_name:
																					__UpperCamelCase      :  int       =   defaults.tpu_name
														if not args.tpu_zone:
																					__UpperCamelCase      :  Optional[Any]       =   defaults.tpu_zone
							if args.accelerate_version == "dev":
														__UpperCamelCase      :  List[str]       =   "git+https://github.com/huggingface/accelerate.git"
							elif args.accelerate_version == "latest":
														__UpperCamelCase      :  Optional[int]       =   "accelerate -U"
							elif isinstance(parse(args.accelerate_version)		,    _lowerCamelCase):
														__UpperCamelCase      :  Union[str, Any]       =   F'accelerate=={args.accelerate_version}'

							if not args.command_file and not args.command:
														raise ValueError("You must specify either a command file or a command to run on the pod.")

							if args.command_file:
														with open(args.command_file		,    "r") as f:
																					__UpperCamelCase      :  List[Any]       =   [f.read().splitlines()]

    # To turn list of lists into list of strings
							if isinstance(args.command[0]		,    _lowerCamelCase):
														__UpperCamelCase      :  Union[str, Any]       =   [line for cmd in args.command for line in cmd]
							# Default to the shared folder and install accelerate
							__UpperCamelCase      :  str       =   ["cd /usr/share"]
							if args.install_accelerate:
														new_cmd += [F'pip install {args.accelerate_version}']
							new_cmd += args.command
							__UpperCamelCase      :  str       =   "; ".join(_lowerCamelCase)

							# Then send it to gcloud
							# Eventually try to use google-api-core to do this instead of subprocess
							__UpperCamelCase      :  List[Any]       =   ["gcloud"]
							if args.use_alpha:
														cmd += ["alpha"]
							cmd += [
							    "compute",
							    "tpus",
							    "tpu-vm",
							    "ssh",
							    args.tpu_name,
							    "--zone",
							    args.tpu_zone,
							    "--command",
							    args.command,
							    "--worker",
							    "all",
							]
							if args.debug:
														print(F'Running {" ".join(_lowerCamelCase)}')
														return
							subprocess.run(_lowerCamelCase)
							print("Successfully setup pod.")




def       _SCREAMING_SNAKE_CASE   (						)   ->       List[Any]:






							'''simple docstring'''





							__UpperCamelCase      :  Optional[Any]       =   tpu_command_parser()
							__UpperCamelCase      :  Optional[Any]       =   parser.parse_args()

							tpu_command_launcher(_lowerCamelCase)